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DeepMind using ledgers to develop a Verifiable Data Audit in healthcare

I’ve written a few times recently about the potential for blockchain to make a dent in the healthcare world. The technology received a boost from when the now chief executive of UK Science and Technology in the British government penned a report bigging up its prospects at the end of last year.

“In the NHS, the technology offers the potential to improve health care by improving and authenticating the delivery of services and by sharing records securely, according to exact rules,” Sir Mark Walport said.

A recent report highlights the potential for blockchain to provide interoperability, integrity and security and portable user-owned data.

“Several innovative start-ups are combining blockchain technology expertise with healthcare industry experience to develop platforms and solutions. This market insight examines the foundations of blockchain, cryptocurrencies, and smart contracts, and presents profiles of start-ups in the industry,” it says.

Moving to the mainstream

It’s perhaps been fair to say that the technology has lacked mainstream support and, crucially, adoption however. That may be changing, not least due to the news that DeepMind have recently announced plans to use blockchain style technology to give all players in the health sector more real-time access to medical data.

The aim is to automatically record every interaction with patient data in a secure manner. The nature of blockchain supports this, as every interaction with a piece of data is recorded.

“[An] entry will record the fact that a particular piece of data has been used, and also the reason why, for example, that blood test data was checked against the NHS national algorithm to detect possible acute kidney injury,” DeepMind say.

There is an understandable desire to create a reliable audit trail for patient data to ensure that patients can have faith that their data is both secure and handled with due care and attention.

Better than blockchain

Whilst the technology used by DeepMind has many similarities to blockchain, they believe their approach is more efficient, as participants aren’t required to use vast quantities of energy to ensure the integrity of the ledger remains strong.

They go on to suggest that health related data doesn’t need to be decentralized, with most data currently stored by a relatively small number of healthcare providers and data processors. This then makes the distributed nature of blockchain rather redundant. To provide the audit trail, the company use a Merkle tree, which is an efficient way of providing a complex audit trail.

Technically, that’s superb, but what is less clear is who will actually own the data that’s recorded, or who will have overall control of the data. DeepMind make the right noises about sound data management in the pledge they request each member of their independent oversight committee signs.

“Patients need to be certain that all their health data is handled with the utmost care and respect, and that their privacy and security are protected at all times. We have strived, and will always strive, to hold ourselves to the highest possible standards of patient data protection and we’re clear about what this looks like,” they say.

But the pledge omits any mention of ownership and access to the data. Indeed, the announcement pointedly says that patients may have direct oversight over their data, but falls some way short of saying they will have ownership of their data.

There is undoubtedly a huge need for data to be better managed within the healthcare industry, both in terms of better synchronizing the various forms of patient data that are being generated, but also the responsible sharing of that data with researchers and clinicians.

Having an industry wide standard and process for data governance may help to achieve that, but having one company appearing to take such a central role does ring alarm bells, especially as the company have been so quiet around the work of their AI ethics committee.

This is especially so as we start adding things such as genetic data to the mix, and with sequencing costs plummeting this seems only a matter of time.